Python Machine Learning Cookbook

Finding patterns in stock market data

Let's see how we can use unsupervised learning for stock market analysis. We will operate with the assumption that we don't know how many clusters there are. As we don't know the number of clusters, we will use an algorithm called Affinity Propagation to cluster. It tries to find a representative datapoint for each cluster in our data. It tries to find measures of similarity between pairs of datapoints and considers all our datapoints as potential representatives, also called exemplars, of their respective clusters. You can learn more about it at http://www.cs.columbia.edu/~delbert/docs/DDueck-thesis_small.pdf

In this recipe, we will analyze the stock market variations of companies in a specified duration ...